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        <h1>
          <span class="m-breadcrumb"><a href="cudaFlowAlgorithms.html">cudaFlow Algorithms</a> &raquo;</span>
          Parallel Iterations
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          <h3>Contents</h3>
          <ul>
            <li><a href="#CUDAForEachIncludeTheHeader">Include the Header</a></li>
            <li><a href="#ForEachCUDAIndexBasedParallelFor">Index-based Parallel Iterations</a></li>
            <li><a href="#ForEachCUDAIteratorBasedParallelIterations">Iterator-based Parallel Iterations</a></li>
            <li><a href="#ForEachCUDAMiscellaneousItems">Miscellaneous Items</a></li>
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<p><a href="classtf_1_1cudaFlow.html" class="m-doc">tf::<wbr />cudaFlow</a> provides two template methods, <a href="classtf_1_1cudaFlow.html#a1a681f6223853b6445dcfdad07e4d0fd" class="m-doc">tf::<wbr />cudaFlow::<wbr />for_each</a> and <a href="classtf_1_1cudaFlow.html#a34f1ea89e5651faa6e8af522a42556ac" class="m-doc">tf::<wbr />cudaFlow::<wbr />for_each_index</a>, for creating tasks to perform parallel iterations over a range of items.</p><section id="CUDAForEachIncludeTheHeader"><h2><a href="#CUDAForEachIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/for_each.hpp</code>, for creating a parallel-iteration task.</p><pre class="m-code"><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;taskflow/cuda/algorithm/for_each.hpp&gt;</span><span class="cp"></span></pre></section><section id="ForEachCUDAIndexBasedParallelFor"><h2><a href="#ForEachCUDAIndexBasedParallelFor">Index-based Parallel Iterations</a></h2><p>Index-based parallel-for performs parallel iterations over a range <code>[first, last)</code> with the given <code>step</code> size. The task created by <a href="classtf_1_1cudaFlow.html#a34f1ea89e5651faa6e8af522a42556ac" class="m-doc">tf::<wbr />cudaFlow::<wbr />for_each_index(I first, I last, I step, C callable)</a> represents a kernel of parallel execution for the following loop:</p><pre class="m-code"><span class="c1">// positive step: first, first+step, first+2*step, ...</span>
<span class="k">for</span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="o">=</span><span class="n">first</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">&lt;</span><span class="n">last</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">+=</span><span class="n">step</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w">  </span><span class="n">callable</span><span class="p">(</span><span class="n">i</span><span class="p">);</span><span class="w"></span>
<span class="p">}</span><span class="w"></span>
<span class="c1">// negative step: first, first-step, first-2*step, ...</span>
<span class="k">for</span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="o">=</span><span class="n">first</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">&gt;</span><span class="n">last</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">+=</span><span class="n">step</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w">  </span><span class="n">callable</span><span class="p">(</span><span class="n">i</span><span class="p">);</span><span class="w"></span>
<span class="p">}</span><span class="w"></span></pre><p>Each iteration <code>i</code> is independent of each other and is assigned one kernel thread to run the callable. Since the callable runs on GPU, it must be declared with a <code>__device__</code> specifier. The following example creates a kernel that assigns each entry of <code>gpu_data</code> to 1 over the range [0, 100) with step size 1.</p><pre class="m-code"><span class="c1">// assigns each element in gpu_data to 1 over the range [0, 100) with step size 1</span>
<span class="n">cudaflow</span><span class="p">.</span><span class="n">for_each_index</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">100</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="p">[</span><span class="n">gpu_data</span><span class="p">]</span><span class="w"> </span><span class="n">__device__</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">idx</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w">  </span><span class="n">gpu_data</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"></span>
<span class="p">});</span><span class="w"></span></pre></section><section id="ForEachCUDAIteratorBasedParallelIterations"><h2><a href="#ForEachCUDAIteratorBasedParallelIterations">Iterator-based Parallel Iterations</a></h2><p>Iterator-based parallel-for performs parallel iterations over a range specified by two STL-styled iterators, <code>first</code> and <code>last</code>. The task created by <a href="classtf_1_1cudaFlow.html#a1a681f6223853b6445dcfdad07e4d0fd" class="m-doc">tf::<wbr />cudaFlow::<wbr />for_each(I first, I last, C callable)</a> represents a parallel execution of the following loop:</p><pre class="m-code"><span class="k">for</span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="o">=</span><span class="n">first</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">&lt;</span><span class="n">last</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w">  </span><span class="n">callable</span><span class="p">(</span><span class="o">*</span><span class="n">i</span><span class="p">);</span><span class="w"></span>
<span class="p">}</span><span class="w"></span></pre><p>The two iterators, <code>first</code> and <code>last</code>, are typically two raw pointers to the first element and the next to the last element in the range in GPU memory space. The following example creates a <code>for_each</code> kernel that assigns each element in <code>gpu_data</code> to 1 over the range <code>[gpu_data, gpu_data + 1000)</code>.</p><pre class="m-code"><span class="c1">// assigns each element to 1 over the range [gpu_data, gpu_data + 1000)</span>
<span class="n">cudaflow</span><span class="p">.</span><span class="n">for_each</span><span class="p">(</span><span class="n">gpu_data</span><span class="p">,</span><span class="w"> </span><span class="n">gpu_data</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1000</span><span class="p">,</span><span class="w"> </span><span class="p">[]</span><span class="w"> </span><span class="n">__device__</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="o">&amp;</span><span class="w"> </span><span class="n">item</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w">  </span><span class="n">item</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"></span>
<span class="p">});</span><span class="w"> </span></pre><p>Each iteration is independent of each other and is assigned one kernel thread to run the callable. Since the callable runs on GPU, it must be declared with a <code>__device__</code> specifier.</p></section><section id="ForEachCUDAMiscellaneousItems"><h2><a href="#ForEachCUDAMiscellaneousItems">Miscellaneous Items</a></h2><p>The parallel-iteration algorithms are also available in <a href="classtf_1_1cudaFlowCapturer.html#a0b2f1bcd59f0b42e0f823818348b4ae7" class="m-doc">tf::<wbr />cudaFlowCapturer::<wbr />for_each</a> and <a href="classtf_1_1cudaFlowCapturer.html#aeb877f42ee3a627c40f1c9c84e31ba3c" class="m-doc">tf::<wbr />cudaFlowCapturer::<wbr />for_each_index</a>.</p></section>
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